The management and upkeep of health records have a significant influence on the effectiveness of healthcare services. Data privacy and security are of utmost importance in the field of medical services. Ensuring data security and secure storage of medical data are key concerns for the majority of individuals. The implementation of a technological platform is crucial in establishing a robust and reliable communication system inside a Healthcare System, ensuring both security and seamless functionality. The technology known as blockchain has undergone significant advancements during the past several years. The primary attributes of Blockchain that attract a significant number of developers include its Distributed Ledger, Decentralization, and Immutability. Hence, the utilization of Blockchain Technology presents a viable solution for securely storing personal medical data. The impact of its utilization in healthcare systems will be substantial. In recent times, there has been a notable change in the manner in which Health Records are stored in Mobile Cloud Environments. This change involves the integration of mobile devices with Cloud Computing, which enables the seamless interchange of medical data between Patients and Providers. The Healthcare system facilitates the provision of healthcare services at reduced operational expenses, while offering considerable adaptability and extensive access to medical data records. The system under consideration encompasses a framework for sharing Medical History, which integrates the use of Blockchain technology into a Desktop Application. Specifically, we have developed a reliable access control mechanism that ensures the secure sharing of health records among diverse individuals and healthcare professionals. In this study, we introduce a prototype implementation that utilizes machine learning techniques to forecast the appropriate disease diagnosis and therapy recommendation for patients based on their reported symptoms. Within this particular system, the utilization of a model facilitates the expeditious examination of patients’ symptoms by medical professionals, enabling them to promptly make informed decisions. Therefore, a machine learning model utilizes patient data to generate informed predictions, so aiding in the process of prescribing appropriate treatments.